Risk-Adjusted Burn Outcome Prediction Across Healthcare Centers
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Solution Overview
Problem
Burn care centers face variability in patient mortality and length of stay (LOS) outcomes, with traditional estimates being inconsistent across patients and centers, hindering quality assessment and improvement.
Innovation Solution
A risk-adjusted statistical model using gradient boosted regression, such as CatBoost, is trained on anonymized patient data from multiple healthcare centers to predict mortality and LOS, providing patient-specific insights and center performance comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional LOS estimation methods (1 day per % TBSA) are used, then the calculation is simple and quick, but the precision and reliability of the estimate deteriorates due to variability across patients and centers
Solution Approach 1:
The patent transforms the traditional single-parameter estimation (1 day per % TBSA) into a multi-parameter predictive model that incorporates patient demographics, burn characteristics, comorbidities, and center-specific factors. This parameter expansion resolves the contradiction by maintaining computational efficiency through structured data processing while dramatically improving LOS estimation accuracy through risk adjustment.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw patient data and LOS prediction. This intermediary layer processes complex interactions among multiple variables, enabling accurate predictions without requiring direct complex calculations. The model acts as a mediator that translates clinical data into precise LOS estimates while accounting for center-specific variations.
2Ease of manufacture
If center-specific traditional estimates are used, then the method is easy to implement at individual centers, but the reliability deteriorates due to lack of standardization across centers
Solution Approach 1:
The patent develops a universal predictive model that functions across multiple burn centers with consistent methodology. The model incorporates center-specific random effects, allowing it to adapt to local characteristics while maintaining standardized prediction algorithms. This universality enables reliable cross-center comparisons and quality assessment while remaining implementable at individual centers through standardized software deployment.
3Measurement precision
If complex machine learning models are trained on large datasets, then the prediction precision improves, but the computational resources and time required increases
Solution Approach 1:
The patent performs preliminary model training on comprehensive datasets during the development phase, creating a pre-trained predictive model that can be deployed for rapid predictions. This preliminary action separates the computationally intensive training process from the prediction phase, allowing high precision predictions to be made with minimal real-time computational resources. The model is prepared in advance to handle complex interactions without requiring extensive computing power during actual patient assessments.
4Device complexity
If traditional mortality assessment methods are used, then the assessment process is simple, but the ability to identify quality improvement opportunities deteriorates due to insufficient granularity
Solution Approach 1:
The patent segments the mortality and LOS prediction into multiple independent components, including patient-level risk factors, center-level effects, and interaction terms. This segmentation allows the model to maintain manageable complexity while capturing detailed information about specific quality drivers. Each segment can be independently analyzed to identify quality improvement opportunities, providing granular insights without overwhelming complexity.
Data Source
AI summary
The disclosed technology includes a method for determining outcomes of patients across healthcare centers, the method including: receiving, at a computer system, patient data for patients in healthcare centers, training, using machine learning techniques and a portion of the data for the burn patients, a predictive model to predict patient outcomes based on assessing patient data for patients across the healthcare centers, and returning the trained predictive model for runtime use. During runtime use, the method can include: providing the patient data as input to the predictive model, receiving, as output, predicted patient outcomes for at least one patient amongst the patients in the healthcare centers, generating, based on the predicted patient outcomes, at least one care recommendation, generating output representative of the predicted patient outcomes and the care recommendation, and transmitting the output to a user computing device for presentation in a graphical user interface (GUI) display.